21 Adding AI to Your Course: Diversity, Equity, and Inclusion (DEI) Research Opportunities

Background

Artificial Intelligence (AI) and Machine Learning (ML) in health care can improve patient outcomes and influence population health related to Diversity, Equity, and Inclusion (DEI). AI can be applied to larger datasets to be more precise when focusing on public health issues by sorting out specific groups in a population (Newton et al., 2018). Another public health benefit of ML is it can analyze patterns of data for surveillance and disease detection, such as foodborne illness, using Google search and phone GPS data (Sadilek et al., 2018). There is a balance between the benefits and the risks of AI and ML.

There are concerns about biases emerging and recommendations to consider inclusivity to avoid health care disparities in different health care specialties (Adamson & Smith, 2018). The speed at which AI and ML health care innovations are emerging requires a shift in teaching and reviewing of data. Medical education is anticipating changes with the implementation of AI, where physicians will need to understand their patient’s relationship with AI and have a greater awareness of social justice issues related to AI (Rampton et al., 2020).There is a balance between the benefits and the risks of AI and ML.

Diversity, equity, and inclusion (DEI) are core values in almost every mission statement in higher education. Higher education and academic conferences are increasingly recognizing that DEI frameworks are fundamental concepts in curriculums, workshops, and events to the point that entire conferences are dedicated to DEI. UF has several conferences that address DEI; some examples include the UF MBA Diversity & Inclusion Conference, Diversity Week at the College of Medicine, and PHHP Diversity, Equity and Inclusion Week. These types of events offer an opportunity to address diversity, equity, and inclusion related to AI and ML and a way to introduce students to research and provide them with a line for their CV or resume to enhance their career opportunities.

In addition, combining an existing AI or ML project with a DEI can help faculty efficiently address DEI and meet some of the core values of the college or university. However, meeting and delivering on these values can be challenging because often, faculty are apprehensive about teaching DEI concepts, fearing they will make mistakes managing difficult conversations such as racism (Lupton & O’Sullivan, 2020; O et al., 2019).

…combining an existing AI or ML project with a DEI can help faculty efficiently address DEI and meet some of the core values of the college or university. This Adding AI to Your Course lesson proposes introducing DEI events in parallel with an existing AI project or creating a new AI project specific to a faculty member’s discipline. The other recommendation is to make the abstract or poster submission optional. Providing a choice and letting students take ownership in the education has been shown to positively influence their learning (Conley & French, 2014; Pawson & Poskitt, 2019).

Additional recommendations and examples will be provided later in this document. Still, faculty should be prepared to discuss the potential for using secondary data and point students towards government agencies that share datasets to get the data in their hands. Secondary data plays an essential role in public health and other disciplines, and having the ability to explore actual data can increase student learning and engagement (Boslaugh, 2007; Spurlock & Spurlock, 2020). There is an additional opportunity to raise awareness of the potential of technology to address the structural, social, environmental, economic, or political factors that contribute to inequities. For this Adding AI to Your Course lesson, the aim is to identify if students will take additional ownership of their education and career development if authentic learning opportunities, instructional modeling, and research instruction are provided in parallel with existing assignments.

Example and Recommendations

Initial Assignment and Lesson Modeling

Motivational Announcement and Additional Secondary Data Part 1

Motivational Announcement and Additional Secondary Data Part 2

Student Examples